{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "np.random.seed(1220)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成模型数据\n",
    "size = 10000\n",
    "x = np.random.normal(size=size)\n",
    "y = (x > 0).astype(np.float64)\n",
    "x *= 1.5\n",
    "x += 0.5 * np.random.normal(size=size)\n",
    "x = x[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = linear_model.LinearRegression()\n",
    "model.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 为在Matplotlib中显示中文，设置特殊字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "# 正确显示负号\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.rcParams.update({'font.size': 13})\n",
    "# 创建一个图形框，在里面画两幅画\n",
    "fig = plt.figure(figsize=(12, 6), dpi=100)\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "ax.set_xlabel('$x$')\n",
    "ax.set_ylabel('$y$')\n",
    "# 将原始数据表现在图上\n",
    "ax.scatter(x[:40], y[:40], color='black')\n",
    "# 画拟合的直线\n",
    "xline = np.linspace(-3, 5, 100)[:, np.newaxis]\n",
    "yline = model.predict(xline)\n",
    "ax.plot(xline.ravel(), yline, 'r')\n",
    "# 第二幅图\n",
    "ax1 = fig.add_subplot(1, 2, 2)\n",
    "residual = y - model.predict(x)\n",
    "n, bins, _ = ax1.hist(residual, 40, density=1, facecolor='grey', rwidth=0.8, alpha=0.6)\n",
    "# 用多项式拟合得到的直方图\n",
    "z1 = np.polyfit(bins[:-1], n, 10)\n",
    "p1 = np.poly1d(z1)\n",
    "ax1.plot(bins[:-1], p1(bins[:-1]), 'r-.')\n",
    "ax1.set_xlabel('$residual$')\n",
    "plt.savefig('logit_example.png', dpi=200)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
